code: def get_varied(n_clusters):
      cluster_std = [1.0, 2.5, 0.5, 1.0, 2.5, 0.5, 1.0, 2.5, 0.5, 1.0][
          :n_clusters
      ]
      assert len(cluster_std) == n_clusters
      X, labels = make_blobs(
          n_samples=N_SAMPLES,
          centers=n_clusters,
          cluster_std=cluster_std,
          random_state=SEED,
      )
      return normalize(X), labels
  
imports: import gradio
  import math
  from functools import partial
  import matplotlib.pyplot
  import numpy
  from sklearn.cluster import AgglomerativeClustering, Birch, DBSCAN, KMeans, MeanShift, OPTICS, SpectralClustering, estimate_bandwidth
  from sklearn.datasets import make_blobs, make_circles, make_moons
  from sklearn.mixture import GaussianMixture
  from sklearn.neighbors import kneighbors_graph
  from sklearn.preprocessing import StandardScaler
label_desc: Use image node_id 7 for calling a global function with example usage: get_varied(n_clusters) and returns: normalize,  labels